No CrossRef data available.
Article contents
Calibration using adaptive model complexity for parallel and fiber-driven mechanisms
Published online by Cambridge University Press: 10 July 2015
Summary
Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.
The paper deals with the development and application of a new adaptive calibration method that extends the geometrical calibration of mechanisms from calibration of only dimensions and kinematical joint positions into the calibration of kinematic joint shape imperfections. Originally unknown nonlinear properties of the kinematical pairs are adaptively included into the kinematical models that are used during the calibration calculation. The method uses description by local linear models and validity functions in order to identify and describe nonlinear properties of the kinematical pairs. During each calibration step, various models with growing complexity are considered before the best model variant is selected to improve calibration results. The method is mainly devoted to the structures with many loops like parallel and fiber-driven parallel mechanisms. The method is applied to parallel mechanism Sliding Star and parallel fiber-driven mechanism Quadrosphere.
Keywords
Information
- Type
- Articles
- Information
- Copyright
- Copyright © Cambridge University Press 2015
References
1.Borgstrom, P. H., “Rapid computation of optimally safe tension distributions for parallel cable-driven robots,” IEEE Trans. Robot. 25 (6), 1271–1281 (2009).Google Scholar
2.Valášek, M. and Karásek, M., “HexaSphere with Cable Actuation,” Recent Advances in Mechatronics 2008–2009, Springer-Verlag, Berlin (2009) pp. 239–244.Google Scholar
3.Lahouar, S., Ottaviano, E., Zeghoul, S., Romdhane, L. and Ceccarelli, M., “Collision free path-planning for cable-driven parallel robots,” Robot. Auton. Syst. - RaS, 57 (11), 1083–1093 (2009).Google Scholar
4.Borgstrom, P. H., “A cable-driven robot with self-calibration capabilities,” IEEE Trans. Robot. 25 (5), 1005–1015 (2009).CrossRefGoogle Scholar
5.Abderrahim, M., Khamis, A., Garrido, S. and Moreno, L., “Acuracy and Calibration Issues of Industrial Manipulators,” Industrial Robotics: Programming, Simulation and Applications. Advanced Robotic Systems International & Pro Literatur Verlag (2006) pp. 131–146.Google Scholar
6.Jauregui, J., Hernandez, E., Ceccarelli, M., Lopez-Cajun, C. and Garcia, A., “Kinematic calibration of precise 6-DOF stewart platform-type positioning systems for radio telescope applications,” Frontiers Mech. Eng. 8 (3), 252–260 (2013).Google Scholar
7.Hernández-Martínez, E. E., López-Cajún, C. S. and Jáuregui-Correa, J. C., “Calibration of parallel manipulators and their application to machine tools,” Ing. Investigación y Tecnol. 11 (2), 141–154 (2010).Google Scholar
8.Mooring, B. W., Roth, Z. S. and Driels, M. R., Fundamentals of Manipulator Calibration, (New York: Wiley, 1991).Google Scholar
10.Renders, J. M., Rossignol, E., Becquet, M. and Hanus, R., “Kinematic calibration and geometrical parameter identification for robots,” IEEE Trans. Robot. Autom. 7 (6), 721–732 (1991).CrossRefGoogle Scholar
11.Angeles, J., “On the nature of the cartesian stiffness matrix,” Ing. Mec. Tecnol. Y Desarrollo, 3 (5), 163–170 (2010).Google Scholar
12.Xuechao, D., Yuanying, Q., Qingjuan, D. and Jingli, D., “Calibration and motion control of a cable-driven parallel manipulator based triple-level spatial positioner,” Adv. Mech. Eng., 2014, Article ID 368018, 1–10 (2014).Google Scholar
13.Chiu, Y. J. and Perng, M. H., “Self-calibration of a general hexapod manipulator with enhanced precision in 5-DOF motions,” Mech. Mach. Theory, 39 (1), 1–23 (2004).Google Scholar
14.Miermeister, P., Pott, A. and Verl, A., “Auto-Calibration Method for Overconstrained Cable-Driven Parallel Robots,” Proceedings of the ROBOTIK 2012; 7th German Conference on Robotics, Munich (May 21–22, 2012) pp. 301–306.Google Scholar
15.Skopec, T., Šika, Z. and Valášek, M., “Measurement and improved calibration of parallel machine sliding star,” Bull. Appl. Mech. 6 (23), 52–56 (2010).Google Scholar
16.Nelles, O., Nonlinear System Identification with Local Linear Neuro-Fuzzy Models PhD Thesis (Darmstadt: Technische Universitat Darmstadt, 1998).Google Scholar
17.Nelles, O., Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models (Berlin: Springer, 2001).Google Scholar
18.Valášek, M., Šika, Z., Zavřel, J., Skopec, T. and Steinbauer, P., “Control Rapid Prototyping of Redundantly Acutated Parallel Kinematical Machine,” Technical Computing Prague (Prague: Humusoft, 2006), pp. 1–10.Google Scholar
19.Šika, Z., Hamrle, V., Valášek, M. and Beneš, P., “Calibrability as additional design criterion of parallel kinematic machines,” Mech. Mach. Theory, 50, 48–63 (2012).Google Scholar
20.Svatoš, P., Šika, Z., Valášek, M., Bauma, V. and Polach, P., “Optimization of anti-backlash fibre driven parallel kinematical structures,” Bull. Appl. Mech. 8 (31), 40–44 (2012).Google Scholar
21.Valášek, M., and Karásek, M., “Kinematical Analysis of HexaSphere,” Proceedings of Engineering Mechanics 2009, Svratka, Czech Republic (May 11–14, 2009) pp. 1371–1378.Google Scholar
22.Valášek, M., Zicha, J., Karásek, J. and Hudec, R., “Hexasphere–Redundantly Actuated Parallel Spherical Mechanism as a New Concept of Agile Telescope. Advances in Astronomy, 2010, Article ID 348286, 1–6 (2010).Google Scholar
23.Sciavicco, L. and Siciliano, B., Modeling and Control of Robot Manipulators (New York: McGraw-Hill, 1996).Google Scholar
24.Stejskal, V. and Valášek, M., Kinematics and Dynamics of Machinery (New York: Marcel Dekker, Inc., 1996).Google Scholar
25.Kurtz, R. and Hayward, V., “Multiple-goal kinematic optimization of a parallel spherical mechanisms with actuator redundancy,” IEEE Trans. Robot. Autom. 8 (5), 644–651 (1992).CrossRefGoogle Scholar
You have
Access
- 3
- Cited by